Differential evolution algorithm to allocate resources

ABSTRACT

Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

TECHNICAL FIELD

The present application generally relates to computer systems and moreparticularly to computer systems that are adapted to accurately and/orautomatically analyze resource allocations.

BACKGROUND

An enterprise may want to analyze a set of resource types. For example,an insurer might want to analyze a portfolio of assets, such as stocks,bonds, hedge fund assets, etc. In particular, the enterprise might wantto optimize an allocation of resources to improve a particular result(e.g., improve net investment income) while satisfying or moreconstraints (e.g., a portfolio duration). When the constraints arelinear, a mean-variance optimization with a quadradic algorithm istypically performed to achieve such a result. In some cases, however,one or more constraints may be non-linear (e.g., a book yield), in whichcase a quadradic algorithm cannot be used.

It would be desirable to provide improved systems and methods toaccurately and/or automatically analyze resource allocations. Moreover,the results should be easy to access, understand, interpret, update,etc.

SUMMARY OF THE INVENTION

According to some embodiments, systems, methods, apparatus, computerprogram code and means are provided to accurately and/or automaticallyanalyze resource allocations in a way that provides fast and usefulresults and that allows for flexibility and effectiveness whenresponding to those results.

Some embodiments are directed to a resource allocation analysis systemimplemented via a back-end application computer server. A resource datastore may contain electronic records associated with a set of resourcetypes, each electronic record including an electronic record identifierand resource parameter. The back-end application computer server mayreceive, from the resource data store, information about a set ofresource types to be analyzed, including the associated resourceparameters. The computer server may then execute a differentialevolutionary algorithm to optimize the set of resource types based on atleast one non-linear constraint and generate resource analysis results.The back-end application computer server may, according to someembodiments, perform a resampling process that uses non-parameterizedhistorical data, regression on at least one resource type, and momentmatching.

Some embodiments comprise: means for receiving, by the back-endapplication computer server from a resource data store, informationabout a set of resource types to be analyzed, including associatedresource parameters, wherein the resource data store contains electronicrecords associated with a set of resource types, each electronic recordincluding an electronic record identifier and resource parameter; andmeans for executing a differential evolutionary algorithm to optimizethe set of resource types based on at least one non-linear constraintand generate resource analysis results. The back-end applicationcomputer server performs a resampling process that usesnon-parameterized historical data, regression on at least one resourcetype, and moment matching.

In some embodiments, a communication device associated with a back-endapplication computer server exchanges information with remote devices inconnection with an interactive graphical user interface. The informationmay be exchanged, for example, via public and/or proprietarycommunication networks.

A technical effect of some embodiments of the invention is an improvedand computerized way to accurately and/or automatically evaluateresource allocations in a way that provides fast and useful results.With these and other advantages and features that will becomehereinafter apparent, a more complete understanding of the nature of theinvention can be obtained by referring to the following detaileddescription and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of a resource allocation analysissystem in accordance with some embodiments.

FIG. 2 illustrates a resource allocation analysis method according tosome embodiments of the present invention.

FIG. 3 is a universe of investible asset classes in accordance with someembodiments.

FIG. 4 is a mean-variance optimization system in accordance with someembodiments.

FIG. 5 shows a traditional mean-variance curve in accordance with someembodiments.

FIG. 6 shows a resampling process according to some embodiments.

FIG. 7 shows a mean-variance resampled curve in accordance with someembodiments.

FIGS. 8A through 8F provide an example of how resampling can improveresults in accordance with some embodiments.

FIG. 9 is a resource allocation analysis display according to someembodiments.

FIG. 10 is a block diagram of an apparatus in accordance with someembodiments of the present invention.

FIG. 11 is a portion of a tabular asset portfolio data store accordingto some embodiments.

FIG. 12 illustrates a tablet computer with resource allocation analysisdisplay according to some embodiments.

DETAILED DESCRIPTION

Before the various exemplary embodiments are described in furtherdetail, it is to be understood that the present invention is not limitedto the particular embodiments described. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only and is not intended to limit the scope ofthe claims of the present invention.

In the drawings, like reference numerals refer to like features of thesystems and methods of the present invention. Accordingly, althoughcertain descriptions may refer only to certain figures and referencenumerals, it should be understood that such descriptions might beequally applicable to like reference numerals in other figures.

The present invention provides significant technical improvements tofacilitate data analytics associated with resource allocation analysis.The present invention is directed to more than merely a computerimplementation of a routine or conventional activity previously known inthe industry as it provides a specific advancement in the area ofelectronic record analysis by providing improvements in the operation ofa computer system that analyzes resource allocations. The presentinvention provides improvement beyond a mere generic computerimplementation as it involves the novel ordered combination of systemelements and processes to provide improvements in the speed and accuracyof such an analysis. Some embodiments of the present invention aredirected to a system adapted to automatically analyze electronicrecords, aggregate data from multiple sources, automatically optimizeresource allocations, etc. Moreover, communication links and messagesmay be automatically established, aggregated, formatted, exchanged, etc.to improve network performance (e.g., by reducing an amount of networkmessaging bandwidth and/or storage required to analyze resourceallocations).

FIG. 1 is a high-level block diagram of a resource allocation analysissystem 100 according to some embodiments of the present invention. Inparticular, the system 100 includes a back-end application computerserver 150 that may access information in a resource data store 110(e.g., storing a set of electronic records associated with an enterprise112, each record including, for example, one or more resourceidentifiers 114, parameters 116, etc.). The back-end applicationcomputer server 150 may also store information into other data stores,such as a result table 120 and utilize a resource allocation analysissystem 155 to view, analyze, and/or update the electronic records. Theback-end application computer server 150 may also exchange informationwith a first remote user device 160 and a second remote user device 170(e.g., via a firewall 165). According to some embodiments, aninteractive graphical user interface platform of the back-endapplication computer server 150 (and, in some cases, enterprise data 130and/or third-party data 132) may facilitate forecasts, decisions,predictions, and/or the display of results via one or more remoteadministrator computers (e.g., to identify an optimized resourceallocation) and/or the remote user devices 160, 170. For example, thefirst remote user device 160 may transmit annotated and/or updatedinformation to the back-end application computer server 150. Based onthe updated information, the back-end application computer server 150may adjust data in the resource data store 110 and/or the result table120 and the change may be viewable via the second remote user device170. Note that the back-end application computer server 150 and/or anyof the other devices and methods described herein might be associatedwith a third party, such as a vendor that performs a service for anenterprise.

The back-end application computer server 150 and/or the other elementsof the system 100 might be, for example, associated with a PersonalComputer (“PC”), laptop computer, smartphone, an enterprise server, aserver farm, and/or a database or similar storage devices. According tosome embodiments, an “automated” back-end application computer server150 (and/or other elements of the system 100) may facilitate theautomated access and/or update of electronic records in the result table120. As used herein, the term “automated” may refer to, for example,actions that can be performed with little (or no) intervention by ahuman.

As used herein, devices, including those associated with the back-endapplication computer server 150 and any other device described herein,may exchange information via any communication network which may be oneor more of a Local Area Network (“LAN”), a Metropolitan Area Network(“MAN”), a Wide Area Network (“WAN”), a proprietary network, a PublicSwitched Telephone Network (“PSTN”), a Wireless Application Protocol(“WAP”) network, a Bluetooth network, a wireless LAN network, and/or anInternet Protocol (“IP”) network such as the Internet, an intranet, oran extranet. Note that any devices described herein may communicate viaone or more such communication networks.

The back-end application computer server 150 may store information intoand/or retrieve information from the resource data store 110 and/or theresult table 120. The data elements 110, 120 may be locally stored orreside remote from the back-end application computer server 150. As willbe described further below, the resource data store 110 may be used bythe back-end application computer server 150 in connection with aninteractive user interface to access and update electronic records.Although a single back-end application computer server 150 is shown inFIG. 1 , any number of such devices may be included. Moreover, variousdevices described herein might be combined according to embodiments ofthe present invention. For example, in some embodiments, the back-endapplication computer server 150 and resource data store 110 might beco-located and/or may comprise a single apparatus.

Note that the system 100 of FIG. 1 is provided only as an example, andembodiments may be associated with additional elements or components.According to some embodiments, the elements of the system 100automatically transmit information associated with an interactive userinterface display over a distributed communication network. FIG. 2illustrates a method 200 that might be performed by some or all of theelements of the system 100 described with respect to FIG. 1 , or anyother system, according to some embodiments of the present invention.The flow charts described herein do not imply a fixed order to thesteps, and embodiments of the present invention may be practiced in anyorder that is practicable. Note that any of the methods described hereinmay be performed by hardware, software, or any combination of theseapproaches. For example, a computer-readable storage medium may storethereon instructions that when executed by a machine result inperformance according to any of the embodiments described herein.

At S210, a back-end application computer server may receive, from aresource data store, information about a set of resource types to beanalyzed, including associated resource parameters. The resource datastore may, according to some embodiments, contain electronic recordsassociated with a set of resource types (e.g., a portfolio of assetsowned by an insurer), and each electronic record may include anelectronic record identifier and resource parameter. The system may thendetermine constraints associated with optimization of the portfolio atS220. The portfolio might include, for example, stocks, bonds, hedgefund assets, high yield corporate assets, emerging market assets,tax-exempt municipal assets, private equity, governmental treasuryassets, cash, etc. If the constraints are linear at S230, the system mayuse a quadradic equation to perform the optimization at S240.

If the constraints are non-linear at S230, the system may use adifferential evolution algorithm to perform the optimization at S250.Examples of non-linear constraints might include, according to someembodiments, capital consumption, asset turnover, book yield, realizedcapital gains, etc. According to some embodiments, the differentialevolution algorithm might optimize Net Investment Income (“NII”) and/orexpected return (e.g., the system might maximize the expected returnwhile keeping the NII at the same level). In particular, the system mayexecute the differential evolutionary algorithm to optimize the set ofresource types based on at least one non-linear constraint and generateresource analysis results. As used herein, the phrase “evolutionaryalgorithm” may refer to a generic model-based metaheuristic optimizationalgorithm. The algorithm may use mechanisms inspired by biologicalevolution, such as reproduction, mutation, recombination, and selection.Candidate solutions to the optimization problem play the role ofindividual models in a population, and a fitness function determines thequality of the solutions (e.g., as defined by a target loss function).Evolution of the models then takes place after the repeated applicationof these operators. Note that evolutionary algorithms often perform wellapproximating solutions to various types of problems because theyideally do not make any assumption about the underlying fitnesslandscape. In many applications, computational complexity of theevolutionary algorithm is a prohibiting factor. Consider, for example, ageneric single-objective evolutionary algorithm. The system mayinitially generate an initial population of models randomly (e.g., thefirst generation). The system may then repeat the followingre-generational steps until termination: (1) evaluate the fitness ofeach model in the population, (2) select the fittest models forreproduction (parents), (3) create new models through crossover andmutation operations to give birth to offspring models, and (4) replacethe least-fit models in the population with new models.

According to some embodiments, a back-end application computer serverperforms a resampling process that uses non-parameterized historicaldata, regression on at least one resource type, and moment matching. Theresampling process might comprise, for example: constructing arisk-return curve using mean-variance optimization; executing resampleswith varied return distribution or confidence intervals; andconstructing a new risk-return curve by averaging the resampled results.As used herein, the phrase “moment matching” may refer to adetermination of a mixture parameters such that the compositedistribution has moments matching some given value. In many instancesextraction of solutions to the moment equations may present non-trivialalgebraic or computational problems. Moment matching might be associatedwith, for example, a mean, a volatility, a skew, a distribution shape,etc.

Portfolio construction in insurance asset management may be centrallyfocused on driving the business objectives of an insurance company,which generally include supporting earnings, allocating capital, andgrowing book value. Objectives and constraints may vary but can bebroadly grouped into three categories: liability funding needs,accounting and regulatory constraints, and economic considerations.Because of this, portfolio optimization for insurance portfolios tendsto be more complicated and incorporate more factors than traditionalasset allocation models.

Portfolio optimization can account for liability funding needs in theform of duration, yield, and/or liquidity targets. Accounting andregulatory restrictions can be managed through limits on net investmentincome impact, expected write downs, and total capital consumption ofsolutions. Economic considerations include investment performance andrisk tolerance, absolute and relative to liabilities, in both normal andstressed environments.

A multitude of portfolio constraints can be incorporated into a model,which can help ensure appropriate levels of diversification, especiallywhen using inputs that carry uncertainty. Typically, constraints includerestrictions on the exposure to an asset class or group of asset classes(e.g., limits on illiquid assets). Constraints on duration, yield,capital, and stress capital may also be reflected in the model. Usingreasonable assumptions for future investment performance, portfolios aregenerated that attempt to satisfy objectives without violatingconstraints.

To generate a portfolio optimization, the system may start with auniverse of investable asset classes. For example, FIG. 3 is a universeof investible asset classes 300 in accordance with some embodiments.Each asset class 300 includes parameters such as those for risk/return,income, duration, capital charge, tax benefit, etc. Expected returns maybe determined using current market valuations combined with tacticalviews. Next, the system may evaluate expected for each asset class 300,including the shape of the expected return distribution and correlationswith other asset classes 300.

A mean-variance optimization is then modeled using all the accompanyingdata. FIG. 4 is a mean-variance optimization system 400 in accordancewith some embodiments. A mean-variance optimization platform 410 with aquadradic algorithm 420 receives risk/variance along with linearconstraints and generates an optimal solution. The mean-varianceoptimization platform 410 may maximize an expected return (the mean)given the portfolio's risk (the variance) and other constraints.Provided all constraints are linear (changes in market values lead toproportional changes in constraints), the quadratic algorithm 420 can beused to in the effort to find the optimal solution. Assumptions mayinclude yield and duration profiles, expected credit losses anddowngrades, tax treatment, capital charges and stress losses.

With respect to potential shortcomings of traditional optimization,traditional mean-variance optimization has been challenged by some inthe industry for three primary reasons: the assumption of normality inreturns, sensitivity to small changes in assumptions, andunder-diversification of portfolio solutions. Investment returns,especially in credit instruments such as corporate bonds and structuredproducts (typically a significant part of an insurance portfolio), havehistorically shown asymmetrical risk, or “long tails.” Ignoring this bysolely optimizing on the expected volatility of a portfolio may resultin solutions that fail to sufficiently account for the true downsiderisk in stressed environments.

Model sensitivity is another potential drawback of traditionalmean-variance optimization due to the uncertainty of expected returns.Even a small change in an input, especially a shift in performance, cancause a significant reallocation. This issue can become more severe asthe number of asset classes increases.

Due to the model sensitivity issue, under-diversification can occur. Forexample, two potential asset classes (A and B) have similarcharacteristics, but the expected return for A is slightly higher thanB. Traditional mean-variance optimization would meaningfully overweightA versus B. In reality, a diversified portfolio should hold similarallocations to these two asset classes. A minor change can even cause anasset class to drop out altogether or a new asset class to appear with asubstantial weight.

An alternate approach to portfolio optimization is designed to produceintuitive and implementable solutions. The backbone of the approach is aprocess called resampling, which involves sampling multiple periods ofreturns from historical data (e.g., 3-year windows), generating anoptimized portfolio, and repeating the exercise hundreds or eventhousands of times. For instance, a resample of history could includemultiple great recessions or any number of dotcom bubbles. Optimizedportfolios may presented in the form of efficient frontiers or “curves,”which include a minimum volatility portfolio, a maximum returnportfolio, and several portfolios in between meant to maximize return atgiven levels of risk.

The resulting allocations are then averaged to generate a diversifiedmodel portfolio that accounts for how history could have unfolded, notjust how it was actually realized. Each point on the resampled efficientfrontier is the average of that point through all resamples. Forexample, FIGS. 5 through 7 show how resampling has the potential toprovide a more robust result by varying the optimization inputs (asshown, for example, in FIGS. 8A through 8F). In this case, the risk andreturn assumptions used to create the initial frontier are too precise,and the new frontier provides a more realistic risk-reward tradeoff. Theprocess can also help mitigate the issues of under-diversification andmodel sensitivity, since small differences in expected returns do notproduce the correspondingly large effects apparent in the originalfrontier's results.

The resampled efficient frontier shows the relationship between theexpected return and the expected risk of portfolios that lie within theclient's constraints. Portfolios are reviewed to understand how theywould have performed through historical stress periods. This isimportant because, during times of stress, historical investment returnsare oftentimes negatively skewed, which can lead to greater losses thananticipated. A final solution is then selected on the frontier, based onthe client's risk tolerance. It is also possible to optimize for minimumrequired capital and maximum net investment income, which is useful inunderstanding the different levers a client can pull when seeking toachieve objectives.

FIG. 5 shows 500 a traditional mean-variance curve in accordance withsome embodiments. A graph shows risk 510 and return 520 and a curve 530is constructed using mean-variance optimization. Note that a singleoptimization result is often under-diversified and sensitive to smallchanges in assumptions. FIG. 6 shows 600 a resampling process accordingto some embodiments. A graph again shows risk 610 and return 620 and thesystem is resampled 630 multiple times. Assumptions, such as returndistribution or confidence intervals, are varied to account for theuncertainty of the sample estimates. FIG. 7 shows 700 a mean-varianceresampled curve 730 (illustrated as a dashed line in FIG. 7 ) on a risk710 versus return 720 graph in accordance with some embodiments. The newcurve 730 is constructed by averaging the resampled frontiers. Thiscurve 730 is built on more realistic assumptions and is betterdiversified as compared to the original curve 530 of FIG. 5 (as shown,for example, in FIGS. 8A through 8F).

FIGS. 8A through 8F provide an example of how resampling can improveresults in accordance with some embodiments. In particular, FIG. 8A is achart 810 providing resource parameters for various asset classes. FIG.8B is a chart 820 showing standard values for various resources(“Resource 1” through “Resource 14”), and FIG. 8C is a chart 830 showingresampled values for those resources. FIG. 8D is a graph 840 shows risk(X-axis) versus return (Y-axis) for the standard values (solid line) andresampled values (dashed line). FIG. 8E is a graph 840 that shows astandard Mean-Variance Optimization (“MVO”), and FIG. 8F is a graph 850that shows a resampled MVO. Note that in a comparison of FIG. 8C(resampled) versus FIG. 8B (not resampled), the diversification acrossthe difference resources increases. For example, at point 10 in themiddle of the curve, the optimization allocates to only three assetclasses in FIG. 8B. In contrast, the comparable point in FIG. 8C mightallocate to seven different resources, while achieving comparable returnand volatility. FIGS. 8E and 8F reinforce this benefit of resamplinggraphically.

Note that there are two algorithms that can be used for optimizingportfolios. A quadratic algorithm will find the closed-form solution aslong as all constraints are linear. When the system includes constraintsthat are non-linear, there is no mathematical way to find the optimalsolution. In these situations, a differential evolution algorithm may beused to approximate the optimal solution. The computational intensity ofthe evolutionary algorithm increases processing time. Thus, quadraticoptimization is favored unless non-linear constraints are necessary, andthe portfolio is at a significant enough unrealized gain or lossposition that linear approximations are unable to generate adequatesolutions.

Asset allocation model results can be utilized in multiple ways. Acommon use is generating a long-term neutral risk position as part of aninvestment policy statement, which acts as a formal representation ofthe strategic asset allocation. This agreed-upon benchmark helps clarifyobjectives and guide investment decisions made on a client's behalf.With the benchmark in place, security selection performance within assetclasses can be evaluated.

The asset allocation model is also useful in tactical positioning.Portfolio managers are often required to make top-level decisions toallocate over short-term timeframes. The model supports the portfoliomanagement function by organizing the multitude of inputs that need tobe incorporated into investment decisions. A primary input to tacticalasset allocation is short-term return forecasts, which can be influencedby the current level of spreads, expected changes in spreads,macroeconomic developments, and evolving views on default and migrationrisks.

Consider an insurance company that completes an acquisition of a newbusiness, which leads to the assumption of new assets under management.The portfolio manager works with the client to understand the incomingportfolio and ascertain objectives and constraints. The asset allocationteam can add value by dimensioning the current portfolio and identifyingopportunities when seeking to better align the strategy with investmentobjectives.

In this example, the current portfolio does not satisfy the incomeobjectives of the new company, while an outsized allocation to preferredequity contributes too much risk. By building portfolio optimizationsthat incorporate income needs, capital constraints, and economicopportunities, the asset allocation team may be able to help create aportfolio that has the potential to enhance income while reducingconcentration risk. The solution reduces public and preferred equitiesin favor of high yield corporate bonds, reduces municipal bonds in favorof investment grade corporate bonds, and selectively pursuesopportunities in illiquid asset classes such as corporate privateplacements and commercial mortgage loans.

As another example, consider client who is changing objectives to beless focused on net investment income and more on statutory capital. Inorder to fund the current liabilities, the investment manager needs toidentify the appropriate amount of capital to hold and determine whichasset classes to invest in. The asset allocation team determines thatwhen the allocation to high-quality structured products and commercialmortgage loans is increased, the expected return and risk profile of theportfolio can be maintained while reducing the required capital.

The data analyzed by the system may be presented on a Graphical UserInterface (“GUI”). For example, FIG. 9 is a resource allocation analysisdisplay 900 including graphical representations of elements of ananalysis system 910 according to some embodiments. Selection of aportion or element of the display 900 might result in the presentationof additional information about that portion or element (e.g., a popupwindow presenting a data source or result table) or let an operator oradministrator enter or annotate additional information about resourceallocations (e.g., based on his or her experience and expertise).Selection of an “Update” icon 950 (e.g., by touchscreen or computermouse pointer 990) might cause the system or platform to re-analyze aportfolio.

The embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 10 illustrates anapparatus 1000 that may be, for example, associated with the system 100described with respect to FIG. 1 . The apparatus 1000 comprises aprocessor 1010, such as one or more commercially available CentralProcessing Units (“CPUs”) in the form of one-chip microprocessors,coupled to a communication device 1020 configured to communicate via acommunication network (not shown in FIG. 10 ). The communication device1020 may be used to communicate, for example, with one or more remotethird-party business or economic platforms, administrator computers,and/or communication devices (e.g., PCs and smartphones). Note thatcommunications exchanged via the communication device 1020 may utilizesecurity features, such as those between a public internet user and aninternal network of an insurance company and/or an enterprise. Thesecurity features might be associated with, for example, web servers,firewalls, and/or PCI infrastructure. The apparatus 1000 furtherincludes an input device 1040 (e.g., a mouse and/or keyboard to enterinformation about data sources, optimization parameters, third-parties,etc.) and an output device 1050 (e.g., to output reports regardinganalysis results, recommended changes, alerts, etc.).

The processor 1010 also communicates with a storage device 1030. Thestorage device 1030 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 1030 stores a program1015 and/or an asset allocation analysis tool or application forcontrolling the processor 1010. The processor 1010 performs instructionsof the program 1015, and thereby operates in accordance with any of theembodiments described herein. For example, the processor 1010 mayexecute a differential evolutionary algorithm to optimize a set ofresource types based on at least one non-linear constraint and generateresource analysis results. The processor 1010 may, according to someembodiments, perform a resampling process that uses non-parameterizedhistorical data, regression on at least one resource type, and momentmatching.

The program 1015 may be stored in a compressed, uncompiled and/orencrypted format. The program 1015 may furthermore include other programelements, such as an operating system, a database management system,and/or device drivers used by the processor 1010 to interface withperipheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the apparatus 1000 from another device; or (ii) asoftware application or module within the apparatus 1000 from anothersoftware application, module, or any other source.

In some embodiments (such as shown in FIG. 10 ), the storage device 930further stores an asset portfolio data store 1100 (e.g., definingcharacteristics of the portfolio), third-party data 1070 (e.g., withthird-party business or economic data), enterprise data 1080 (e.g.,regarding asset allocations, characteristics, constraints, etc.), and aresult table database 1090. An example of database that might be used inconnection with the apparatus 1000 will now be described in detail withrespect to FIG. 11 . Note that the database described herein is only anexample, and additional and/or different information may be storedtherein. Moreover, various databases might be split or combined inaccordance with any of the embodiments described herein. For example,the result table database 1090 might be combined and/or linked to eachother within the program 1015.

Referring to FIG. 11 , a table is shown that represents the assetportfolio data store 1000 that may be stored at the apparatus 1000according to some embodiments. The table may include, for example,entries associated with assets owned by an enterprise. The table mayalso define fields 1102, 1104, 1106, 1108, 1110 for each of the entries.The fields 1102, 1104, 1106, 1108, 1110 may, according to someembodiments, specify: a portfolio identifier 1102, an asset class 1104,a risk/return 1106, an income 1108, and a tax benefit 1110. The assetportfolio data store 1100 may be created and updated, for example, basedon information electrically received from various operators,administrators, and computer systems (e.g., including when a newportfolio is obtained or analyzed) that may be associated with aninsurer.

The portfolio identifier 1102 may be, for example, a unique alphanumericcode identifying a set or resources or assets to be analyzed. The assetclass 1104 may describe the asset. The asset portfolio data store 1100may include various resource parameters for the asset class 1104, suchas the risk/return 1106, the income 1108, the tax benefit 1110, etc.

Thus, embodiments may provide an automated and efficient way to analyzea set of resources even when some optimization constraints arenon-linear. The following illustrates various additional embodiments ofthe invention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, note that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the displays described herein might beimplemented as a virtual or augmented reality display and/or thedatabases described herein may be combined or stored in externalsystems). Moreover, although embodiments have been described withrespect to specific types of enterprises, embodiments may instead beassociated with other types of enterprises in additional to and/orinstead of those described herein. Similarly, although certain assetclasses and parameters were described in connection some embodimentsherein, other types of asset classes and parameters might be usedinstead.

Note that the displays and devices illustrated herein are only providedas examples, and embodiments may be associated with any other types ofuser interfaces. For example, FIG. 12 illustrates a tablet computer 1200with a resource allocation analysis display 1210 according to someembodiments. The resource allocation analysis display 1210 showselements of a portfolio analysis system that might include selectabledata that can be modified by a user of the handheld computer 1200 (e.g.,via an “Update” icon 1250) to view updated resource allocation analysisresult data associated with an enterprise (e.g., including, in someembodiments, optimized allocations).

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

1. A resource allocation analysis system implemented via a back-end application computer server, comprising: (a) a resource data store associated with an encrypted database management system and containing electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; (b) the back-end application computer server, coupled to the resource data store, including: a computer processor, and a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the back-end application computer server to: (i) receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters, (ii) if a constraint type is non-linear: execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint associated with net investment income and generate resource analysis results, and (iii) if a constraint type is linear: execute a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; and (c) a communication port coupled to the back-end application computer server to facilitate a transmission of data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
 2. The system of claim 1, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
 3. The system of claim 1, wherein the moment matching is associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
 4. The system of claim 1, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
 5. The system of claim 4, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
 6. The system of claim 4, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
 7. The system of claim 4, wherein the differential evolution algorithm further optimizes expected return.
 8. A computerized resource allocation analysis method implemented via a back-end application computer server, comprising: receiving, by the back-end application computer server from a resource data store, information about a set of resource types to be analyzed, including associated resource parameters, wherein the resource data store is associated with an encrypted database management system and contains electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; if a constraint type is non-linear: executing a differential evolutionary algorithm to optimize the set of resource types based on the at least one non-linear constraint associated with net investment income and generate resource analysis results, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; if a constraint type is linear: executing a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm; and transmitting, via a communication port coupled to the back-end application computer server, data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
 9. The method of claim 8, wherein the set of resource types are optimized based on at least one non-linear constraint and the differential evolutionary algorithm is executed.
 10. The method of claim 9, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
 11. The method of claim 9, wherein the moment matching is further associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
 12. The method of claim 9, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
 13. The method of claim 12, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
 14. The method of claim 12, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
 15. The method of claim 12, wherein the differential evolution algorithm further optimizes expected return.
 16. A non-transitory, computer-readable medium storing instructions, that, when executed by a processor, cause the processor to perform a resource allocation analysis method implemented via a back-end application computer server, the method comprising: receiving, by the back-end application computer server from a resource data store, information about a set of resource types to be analyzed, including associated resource parameters, wherein the resource data store is associated with an encrypted database management system and contains electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; if a constraint type is non-linear: executing a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint associated with net investment income and generate resource analysis results, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; if a constraint type is linear: executing a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm; and transmitting, via a communication port coupled to the back-end application computer server, data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
 17. The medium of claim 16, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
 18. The medium of claim 16, wherein the moment matching is associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
 19. The medium of claim 16, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
 20. The medium of claim 19, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
 21. The medium of claim 19, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
 22. The medium of claim 19, wherein the differential evolution algorithm further optimizes expected return. 